2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023
DOI: 10.1109/iros55552.2023.10341993
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Real-Time RRT* with Signal Temporal Logic Preferences

Alexis Linard,
Ilaria Torre,
Ermanno Bartoli
et al.
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Cited by 6 publications
(4 citation statements)
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“…The real-time performance of the improved RRT in a static scenario is shown in 3.4.1, and even for dynamic scenarios, the improved algorithm still shows its superior real-time performance. For example, STL-RT-RRT∗ [ 192 ] and RT-RRT∗ [ 193 ] can react to moving obstacles in real scenarios (1.1 m/s for moving obstacles and 0.55 m/s for the robot). STL-RT-RRT∗ even achieves 100 % obstacle avoidance success rate in 1000 trials, with a failure chance of less than 0.1 %.…”
Section: Overview Of Rrt-based Algorithm Improvementsmentioning
confidence: 99%
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“…The real-time performance of the improved RRT in a static scenario is shown in 3.4.1, and even for dynamic scenarios, the improved algorithm still shows its superior real-time performance. For example, STL-RT-RRT∗ [ 192 ] and RT-RRT∗ [ 193 ] can react to moving obstacles in real scenarios (1.1 m/s for moving obstacles and 0.55 m/s for the robot). STL-RT-RRT∗ even achieves 100 % obstacle avoidance success rate in 1000 trials, with a failure chance of less than 0.1 %.…”
Section: Overview Of Rrt-based Algorithm Improvementsmentioning
confidence: 99%
“…In Ref. [ 192 ], a car-like robot on a miniature circular runway at a speed of 0.17 m/s successfully avoid the moving obstacles across the runway. In simple dynamic scenarios, the average dynamic obstacle avoidance success rate of the robotic arm is 100 % and the average path generation time is less than 0.1s [ 161 ].…”
Section: Overview Of Rrt-based Algorithm Improvementsmentioning
confidence: 99%
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